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Gaussian Rate-Distortion-Perception Coding and Entropy-Constrained Scalar Quantization

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arxiv 2409.02388 v1 pith:25CA4GUE submitted 2024-09-04 cs.IT cs.LGmath.IT

classification cs.ITcs.LGmath.IT
keywords perceptionboundsmeasurecodingentropy-constrainedestablishedgaussianother
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This paper investigates the best known bounds on the quadratic Gaussian distortion-rate-perception function with limited common randomness for the Kullback-Leibler divergence-based perception measure, as well as their counterparts for the squared Wasserstein-2 distance-based perception measure, recently established by Xie et al. These bounds are shown to be nondegenerate in the sense that they cannot be deduced from each other via a refined version of Talagrand's transportation inequality. On the other hand, an improved lower bound is established when the perception measure is given by the squared Wasserstein-2 distance. In addition, it is revealed by exploiting the connection between rate-distortion-perception coding and entropy-constrained scalar quantization that all the aforementioned bounds are generally not tight in the weak perception constraint regime.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Rate-Distortion-Perception Theory for the Quadratic Wasserstein Space

    cs.IT 2025-04 conditional novelty 7.0 of 10

    A single-letter formula characterizes the distortion-rate-perception tradeoff with limited common randomness, with explicit Gaussian evaluations and a universality analysis.

  2. Channel-Aware Optimal Transport: A Theoretical Framework for Generative Communication

    cs.IT 2024-12 conditional novelty 7.0 of 10

    Without common randomness, a hybrid coding scheme can beat both separation-based and uncoded architectures for channel-aware optimal transport on binary and Gaussian channels.

  3. Easz: An Agile Transformer-based Image Compression Framework for Resource-constrained IoTs

    eess.IV 2025-05 conditional novelty 5.0 of 10

    Easz compresses images by erasing and squeezing patches on the edge, then reconstructs them on a server with a lightweight transformer at flexible ratios and low edge cost.

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